Topic outline

  • The course is organized remotely. The lectures are prerecorded and published at the Materials section of the course's MyCourses homepage (at the latest) on Mondays and Wednesdays. There will be no actual exercise sessions, but the assistant is on call in Zulip chat on Fridays at 12-16 starting on March 5. You are of course welcome to ask questions and discuss in Zulip at other times too, but responses may be delayed.

    Join the Zulip chat using your Aalto email-address:

    The preferable way to register is via WebOodi.

    The students are assumed to participate actively in the course by weekly returning their solutions to one home assignment (typically involving MATLAB computations). 25% of the overall grade is based on the home assignments and 75% on a home exam.

    The preliminary weekly timetable is as follows:

    • Week 1: Motivation and (truncated) singular value decomposition
    • Week 2: Morozov discrepancy principle and Tikhonov regularization
    • Week 3: Regularization by truncated iterative methods
    • Week 4: Motivation and preliminaries of Bayesian inversion, preliminaries of sampling
    • Week 5: Prior models, Gaussian densities, MCMC (Metropolis-Hastings algorithm)
    • Week 6: MCMC (Gibbs sampler), hypermodels